Neuroimaging methodologies, including electroencephalography (EEG), event-related potentials (ERPs), and both structural and functional magnetic resonance imaging (s/fMRI) have emerged as significant predictors of substance use relapse. These neuroimaging techniques have helped provide an objective, sensitive, and predictive measure of substance use relapse propensity. As the capabilities of biological science advance, a lingering impediment to progress is the study of individual brain regions implicated in predicting substance use relapse. Important to note, individual brain regions rarely work independently; rather, such regions often work in concert, as specific networks, continually sending and receiving information to other regions, to help facilitate learning, memory, and other cognitive processes. The investigation of ICA-derived functional network connectivity between brain regions may lend incremental utility for classifying, diagnosing, and predicting substance use relapse. Here, using the world?s largest forensic database, which includes clinical and neuropsychological measures, electrophysiological measures, functional neuroimaging, and functional network connectivity measures, the primary goal of this project will be to delineate specific risk factors predictive of eventual stimulant use relapse propensity. This will be accomplished by integrating models incorporating logistic and Cox proportional-hazard regressions, and cross-validation machine learning pattern classifiers to predict stimulant use relapse one year after institutional release with an at-risk sample of adult incarcerated offenders. First, we will examine the potential of various clinical and neuropsychological assessment measures in predicting stimulant use relapse. Second, we will investigate the predictive utility of error-monitoring neural measures using traditional ERPs and fMRI in predicting stimulant use relapse. Third, we will apply more agnostic, data-driven methods, including ICA-derived functional network connectivity analyses of fMRI data to measure functional connectivity between brain regions involved in error-processing to predict stimulant use relapse. Ultimately, this research will lend incremental utility in the successful prediction of stimulant use relapse, providing a more sensitive and predictive measure compared to previous investigations. The ultimate goal of the proposed training fellowship is to help provide a more biologically informed taxonomy of neurocognitive deficits associated with those prone to stimulant use relapse.